How to run Mistral 7B on Apple Silicon (M1 and M2)

How to run Mistral 7B on Apple Silicon (M1 and M2)
Image Credit: Mistral AI

A few days ago, Mistral AI announced their Mistral 7B LLM. This announcement caught my attention for two reasons: 1. Mistral claimed that this model could outperform Llama2 13B with almost half the number of parameters, and 2. this is the first LLM of this quality (that I know of) that is truly "free" for all uses given its release under the Apache 2.0 license.

I decided to get it running on my Mac Studio and was surprised to see that no one had published a "how-to" guide yet. Thankfully, the LLM space is teeming with talented individuals who are passionate about spreading knowledge of this amazing technology so I was able to stitch together a few articles and come up with the end-to-end tutorial below. I hope you find it useful!

For reference, these are the information sources I used to get the model running on my Mac and create this tutorial:

  1. Open a terminal window and execute the following commands to install prerequisite programs:
  • xcode-select --install (Only required if you don't have Xcode installed.)
  • brew install pkgconfig cmake to install required tools to build the llammacpp source code. (Make sure you have Homebrew installed first.)
  • brew install poetry to install Poetry. Alternatively, use the official installer.
  1. Create a directory, cd into it, and initialize Poetry by running the following commands:
  • mkdir mistral-test
  • cd mistral-test
  • poetry init

4.Download the llamacpp source code repository and compile llammacpp.

  • git clone
  • cd llama.cpp
  • make
  1. Install the required Python packages.
  • poetry add torch torchvision huggingface-hub
  1. Download a quantized Mistral 7B model from TheBloke's HuggingFace repository. If your Mac has 8 GB RAM, download mistral-7b-instruct-v0.1.Q4_K_M.gguf. For Macs with 16GB+ RAM, download mistral-7b-instruct-v0.1.Q6_K.gguf. (Feel free to experiment with others as you see fit, of course. These are just the ones that make sense to me for each amount of RAM.)
  • huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.1-GGUF mistral-7b-instruct-v0.1.Q6_K.gguf --local-dir ./models --local-dir-use-symlinks False (Remember to change the model name according to your RAM amount.)
  1. Run the model inference.
  • cd .. to navigate back up into the llamma.cpp directory.
  • ./main -m ./models/mistral-7b-instruct-v0.1.Q6_K.gguf -t 8 -n 128 -p 'Q: Who was the first man on the moon? ’ to do a test inference.
  1. Play around with the model!
  • At this point you have a fully working method of talking to the Mistral 7B LLM. Try changing the prompt in the command above and see how it answers!
  1. [OPTIONAL] Run the model with Python.
  • cd .. to change directories back up into the mistral-test directory.
  • poetry add 'llama-cpp-python[server]' to install the required package that allows us to use llammacpp from Python.
  • Open your favorite code editor (or notepad if you like to live dangerously) inside the mistral-test directory.
  • Create a file, add the following code, and save:
from llama_cpp import Llama
llm = Llama(model_path="./llamma.cpp/models/mistral-7b-instruct-v0.1.Q6_K.gguf")
output = llm("Q: Who was the first man on the moon? A: ", max_tokens=32, stop=["Q:", "\n"], echo=True )
  • poetry shell
  • python3 (If you get missing dependency errors, make sure that your interpreter path points to Poetry's interpreter. You can get this path by running poetry show -v.)

Have fun!

Also, if you want to learn more about Mistral AI, check out this TechCrunch article.